Topic: topological phases

Yidun Wan constructs Hamiltonians that connect parent and condensed string-net models to describe bosonic anyon condensation in topological phases. The framework classifies possible condensations and identifies the degrees of freedom of the resulting models. Its microscopic categorical description refines the usual modular-tensor-category perspective and gives algorithms for calculating the categorical quantities through physical model data.

José Ramón Pareja Monturiol presents a method that converts black-box functions into tensor-train representations using sketching, cross interpolation and a small sample of the relevant domain. For neural networks, this enables privacy-oriented obfuscation and interpretable estimates of topological phases. The method also supplies an efficient initialisation for tensor-train optimisation and improves the memory–computation trade-off in model compression.